Cover Page. The handle holds various files of this Leiden University dissertation.

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1 Cover Page The handle holds various files of this Leiden University dissertation. Author: Rooden, Stephanie Maria van Title: Clinical patterns in Parkinson s disease Date:

2 3 A comprehensive model of Health-related Quality of Life in Parkinson s disease: challenges for patient management M. Visser 1, S.M. van Rooden 1, D. Verbaan 1, J. Marinus 1, A.M. Stiggelbout 2 *, J.J. van Hilten 1 * *both authors contributed equally to this study 1 Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands. 2 Department of Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands. Published in Journal of Neurology 2008;255:

3 Abstract Objective: Insight in how impairments and disabilities related to Parkinson s disease (PD) influence health related quality of life (HRQoL) is required to review adequacy of current management strategies. Methods: The Scales for Outcomes in Parkinson s disease (SCOPA) evaluation was used to assess impairments and disabilities. HRQoL was assessed with the EuroQol-5D Visual Analogue Scale. 378 patients with PD who participated in the SCOPA/PROPARK cohort were assessed while on their usual treatment. Multiple linear regression analysis and structural equation modelling were used to construct a model of factors that influence HRQoL. Results: A model with good fit was constructed that identified various impairments and disabilities as important contributors to HRQoL in PD. Of the disabilities, psychosocial well-being had a larger impact on HRQoL than physical functioning. Of the impairments, depression had the largest contribution to HRQoL, followed by axial motor symptoms, gastrointestinal symptoms, and urinary symptoms. In addition, pain, psychiatric and motor complications, and daytime sleepiness had small but significant influences on HRQoL. Conclusions: Multiple factors, including disabilities, nonmotor symptoms and axial motor symptoms, affect HRQoL in patients with PD. In patients who are on symptomatic treatment aiming to alleviate mainly motor symptoms, there is a large impact on HRQoL of nonmotor and nondopaminergic symptoms. Research is warranted to develop and evaluate management strategies for the aspects that currently impact on HRQoL as psychosocial well-being, depressive symptoms, axial motor symptoms, gastrointestinal symptoms, and urinary symptoms. These findings call for a multidisciplinary approach in the care of these features. Introduction Parkinson s disease (PD) is generally known as a movement disorder in which dopamine replacement therapy may alleviate some of the motor symptoms early in the disease but eventually fails with the progression of the disease. However, there is now an increasing awareness that the clinical spectrum is much broader, encompassing also many nonmotor features including depression, autonomic dysfunction, cognitive dysfunction, night-time sleep problems and daytime sleepiness. 1 Additionally, dopaminergic treatment may induce both motor and psychiatric complications. Together, the debilitating effects of PD and its therapy have a considerable impact on health, physical and psychosocial well-being, and are associated with a decrease in health-related quality of life (HRQoL). HRQoL is defined as those aspects of self-perceived well-being that are related to or affected by the presence of disease or its treatment. 2 Numerous factors impact on HRQoL in PD, including disease severity, disease duration, postural instability and falls, motor complications, depression, anxiety, pain, sleep, cognitive impairment, hallucinations and problems with activities of daily life (ADL). 3-5 However, for some of these factors, like motor complications, studies have yielded inconsistent findings. 6,7 Furthermore, many studies have explored relations between one or two domains and HRQoL without taking into account the broadness of the clinical spectrum of PD or the complex interplay of domains that make up the pathway that links impairments to disability to HRQoL. The Scales for Outcomes in Parkinson s disease (SCOPA) model is a comprehensive evaluation of PD that is based on the disablement process: a pathway linking impairments, disability and global outcomes of health. 8 Using the SCOPA evaluation, we aimed to identify which impairments and disabilities contribute to HRQoL and to construct a model based on these disease-specific determinants in PD. 36 A comprehensive model of Health-related Quality of Life in Parkinson s disease

4 Methods Study design The study is part of the PROfiling PARKinson s disease (PROPARK) study, a longitudinal cohort study of patients with PD, who are profiled on phenotype, genotype, disability, and global outcomes of health. Valid and reliable measurement instruments for the different domains of PD were derived from the SCOPA project. ( Data obtained from the first annual evaluation of 420 patients who were included in the period from May 2003 to March 2006 was used for analysis. Participants All patients fulfilled the United Kingdom Parkinson s Disease Society Brain Bank criteria for idiopathic PD. 9 Age-at-onset and disease duration are important determinants of disease course in PD and are related to various manifestations of the disease. 10,11 To obtain an adequate distribution of these characteristics across the cohort, we constructed four strata, based on age-at-onset (onset of the first symptoms as perceived by the patient (</ > 50 years)) and disease duration (< / > 10 years). Recruitment stopped if approximately 100 patients per stratum were included. The principal centre (Leiden University Medical Centre (LUMC) recruited 186 patients (44%), other university hospitals recruited 54 patients (13%) and regional hospitals recruited 180 patients (43 %). No other selection criteria were applied. The study was approved by the medical ethical committee of the LUMC and all participants gave informed consent. Outcome measures Information was obtained on clinical and sociodemographic variables and included age-at-onset, disease duration, disease severity measured with the Hoehn and Yahr scale (H&Y), 12 medication, falls in the last year, age, marital stage, educational level, and employment status. Levodopa equivalent (LDE) units were calculated according to the formula described by Esselink. 13 The following domains were assessed: Impairments: Motor symptoms and motor complications (SPES/SCOPA, sections motor symptoms (MS) and motor complications (MC)), 14 cognitive dysfunction (SCOPA-COG) 15 and Mini Mental State Examination (MMSE), 16 psychiatric complications (SCOPA-PC), 17 depressive symptoms (Beck Depression Inventory (BDI)), 18 night-time sleep problems (NS) and daytime sleepiness (DS) (SCOPA- SLEEP sections NS and DS), 19 autonomic dysfunction (SCOPA-AUT), 20 and pain (Visual Analogue Scale (VAS) for pain). Patients were asked to rate their average pain in the last month on a line ranging from 0 (no pain) to 100 (worst imaginable pain). Disability: Activities of Daily Living (SPES/SCOPA-ADL) 14 and psychosocial wellbeing (SCOPA-PS). 21 Quality of Life: HRQoL was measured using the VAS from the EuroQoL (EQ-5D), a generic HRQoL instrument. 22 Patients were asked to rate their current health status on a line ranging from 0 (death) to 100 (best imaginable health state). Except for the SCOPA-COG and the EQ-VAS, higher scores reflect more problems. Data were collected by means of self-report questionnaires (SCOPA-SLEEP NS and DS, SCOPA-AUT, BDI, SCOPA-PS, EQ-VAS, VAS-PAIN), which patients completed at home two weeks before their assessment. Furthermore, a trained researcher assessed the SPES/SCOPA sections MS, MC, and ADL, SCOPA-COG, and the modified PPRS. A partner, relative, or caregiver was requested to be present during the examination. The majority of the patients were assessed at the LUMC. To avoid bias towards recruiting less severely affected patients, patients who were unable to come to the hospital were assessed at home. Chapter 3 37

5 Statistical analysis If patients had 25% or more missing data on one of the impairment or disability domains, they were excluded from the analysis. If patients had less then 25% missing data on a scale, the missing data were imputed by the mean values of the non-missing items. Means and standard deviations for all impairment and disability domains and HRQoL were calculated. Pearson correlations were calculated between all impairment and disability domains and HRQoL. Path analysis was used to test the linkages among model variables. 23 Estimating path coefficients: Using SPSS 14.0, path coefficients were estimated using multiple linear regressions for the following dependent variable in the model: the (1) SPES/SCOPA-ADL and (2) SCOPA-PS as dependent variable and all impairment domains as independent, and (3) the EQ-VAS as dependent variable and all impairment and disability domains as independent variables. Nonsignificant path coefficients were excluded from the model. Multiple linear regressions were also performed using subdomain scores instead of the total score for domains that significantly contributed to the model, in order to get more insight in their contribution. The SPES/SCOPA-MS was divided into the subdomains bradykinesia-rigidity (items 3a,b + 4a,b), tremor (items 1a,b + 2a,b), and axial symptoms (items 5,6,7,8,9,10). The SPES/SCOPA-MC was divided into dyskinesias (items ) and motor fluctuations (items ). The SCOPA- AUT consists of the subdomains Gastro-intestinal (GI), Urinary (UR), Thermoregulatory (TR), Cardiovascular (CV), Pupillomotor (PM), and Sexual Dysfunction (SX). Model fitting: The overall fit of the final model was assessed using the structural equation modelling program EQS 6.1 for Windows. 24 Multiple indices can be calculated that show how well the data fit the model. The chi-square test for goodness-of-fit should be nonsignificant (indicating that the model does not differ from the data) but is sensitive to sample size. Five other goodness-of-fit indices were evaluated. The Bentler-Bonnet normed fit index (NFI), the nonnormed fit index (NNFI), and the comparative fit index (CFI) range between 0 and 1, whereby 0.90 is the minimally acceptable value, with 0.95 being the minimum if the chi-square test is significant. The root means square error of approximation (RMSEA) estimates the lack of fit in a model compared to a perfect model; above 0.1 indicates a poor fit, under 0.08 indicates a reasonable fit, and under 0.05 indicates a good fit. 25 The standardized root mean square residual (SRMR) indicates good fit if the value is less than Results Patients Forty-two patients (10%) had more then 25% missing data in one of the scales and were excluded from the analysis. Complete data was obtained from 378 PD patients (66% men). The mean (SD) age was 60.0 (11.2) years and the mean (SD) disease duration was 10.2 (6.4) years (Table 1). Patients who were excluded from the analysis because of missing data were significantly older, had longer disease duration, a higher H&Y stage, and were more often female. Furthermore, these patients had a significant lower HRQoL (worse) and scored higher (worse) on all other domains. The mean scores of the impairment and disability domains and HRQoL are presented in Table 2. Correlations and multiple linear regression The SPES/SCOPA-ADL had high correlations with the SPES/SCOPA-MS (r=0.67) and the SCOPA-AUT (r=0.54) (Table 3). The SCOPA-PS had the highest correlations with the BDI (r=0.69) and the SCOPA- 38 A comprehensive model of Health-related Quality of Life in Parkinson s disease

6 AUT (r=0.57), whereas the EQ-VAS for HRQoL had the highest correlations with the SCOPA-PS (r=-0.59) and the BDI (r=-0.56). Table 1. Patient characteristics N 378 men/women (%man) 250/128 (66.1%) Age, yrs (SD) 60.0 (11.2) Years of education (SD) 12.1 (4.1) Patient with partner (%) 310 (82.0 %) Employment status: employed not employed 103 (27.2%) 275 (72.8%) Disease duration, yrs (SD) 10.2 (6.4) Age onset, yrs (SD) 49.8 (11.8) H&Y stages 1 / 2/ 3/ 4/ 5 / missing 15 / 190 /105 / 59 / 4 / 3 MMSE (SD) (N< 24, %) 27.0 (2.5) (35, 9.3 %) Falls in last year (SD) 1.3 (61.5) Patients on levodopa, N (%) 246 (65%) Patients on dopamine agonist, N (%) 266 (70%) Levodopa equivalent units (mg) (460.9) Family history of PD (%) 98 (25.9%) H&Y, Hoehn and Yahr ; MMSE, Mini Mental State Examination Table 2. Outcome measures Domain Outcome measure (scale range) Mean (SD) Motor symptoms SPES/SCOPA-MS (0-42) 13.1 (4.6) Motor complications SPES/SCOPA-MC (0-12) 1.6 (2.4) Cognitive dysfunction SCOPA-COG (0-43) 26.2 (5.9) Depressive symptoms BDI (0-63) 9.8 (6.3) Autonomic dysfunction SCOPA-AUT (0-69) 17.5 (8.4) Psychiatric Complications Modified PPRS (0-18) 2.0 (1.9) Night-time sleep problems SCOPA-SLEEP NS (0-15) 4.4 (3.7) Daytime sleepiness SCOPA-SLEEP DS (0-18) 4.7 (3.7) Pain VAS-PAIN (0-100) 28.8 (25.2) ADL SPES/SCOPA-ADL (0-21) 8.6 (3.3) Psychosocial function SCOPA-PS (0-33) 8.4 (4.9) Health related Quality of Life EQ-VAS (0-100) 67.8 (14.2) SPES/SCOPA-MC: SPES/SCOPA motor complications; BDI: Beck Depression Inventory; Modified PPRS: Modified Parkinson Psychosis Rating Scale; SCOPA-SLEEP NS: SCOPA-SLEEP Night-time sleep; SCOPA- SLEEP DS: SCOPA-SLEEP Daytime sleepiness; SPES/SCOPA-ADL: SPES/SCOPA Activities of Daily Living; SCOPA-PS: SCOPA-Psychosocial functioning; EQ-VAS: EuroQol-Visual Analogue Scale Chapter 3 39

7 Table 3. Pearson correlation matrix of impairment and disability domains and health related quality of life EQ-VAS SPES/SCOPA-ADL SCOPA-PS SPES/SCOPA-MS SPES/SCOPA-MC SCOPA-COG BDI SCOPA-AUT SCOPA-PC SCOPA-SLEEP NS SCOPA-SLEEP DS VAS-PAIN SPES/SCOPA-ADL SCOPA-PS SPES/SCOPA-MC: SPES/SCOPA motor complications; BDI: Beck Depression Inventory; Modified PPRS: Modified Parkinson Psychosis Rating Scale; SCOPA-SLEEP NS: SCOPA-SLEEP Night-time sleep; SCOPA- SLEEP DS: SCOPA-SLEEP Daytime sleepiness; SPES/SCOPA-ADL: SPES/SCOPA Activities of Daily Living; SCOPA-PS: SCOPA-Psychosocial functioning; EQ-VAS: EuroQol-Visual Analogue Scale 1 correlation is significant at the level 2 correlation is significant at the 0.01 level 3 correlation is significant at the 0.05 level The impairment domains motor symptoms, motor complications, autonomic dysfunctions, and daytime sleepiness explained 57% of the variance in the ADL domain. The impairment domains depressive symptoms, psychiatric complications, motor symptoms, and autonomic dysfunction explained 58% of the variance in the PS domain. The disability domains ADL and PS and the impairment domains pain and depressive symptoms together explained 43% of the variance in the HRQoL. Two impairment domains that did not significantly contribute to this model were cognitive dysfunction and night-time sleep problems. The group of patients that was excluded because of missing values had significant lower SCOPA- COG scores (26.2 (5.9) versus 20.8 (7.2) p=0.000) and higher SCOPA-SLEEP NS scores (4.4 (3.7) versus 6.0 (4.0) p=0.010). However, the correlation between SCOPA-COG and EQ-VAS were the same in both groups, r=0.24, whereas the correlation between the SCOPA-SLEEP NS and EQ-VAS was lower for the group that was excluded (r=-0.33 versus r =-0.08). Model evaluation The overall fit of the final model that encompassed only the significant paths was assessed using EQS. The chi-square test was significant, which was expected given the large sample size. The other fit indices, however, fulfilled the cut-off criteria of a good model fit (Table 4). Except for depressive symptoms and pain, which have a direct influence on HRQoL, most impairment domains have an indirect relation through the disability level (Figure 1). 40 A comprehensive model of Health-related Quality of Life in Parkinson s disease

8 Table 4. Summary of final model fit statistics 2 (df) (12) P= NFI NNFI CFI SRMR RMSEA (90% CI) ( ) 2, model chi-square value; df, model degrees of freedom; NFI, Bentler-Bonett normed fit index; NNFI, Bentler-Bonett non-normed fit index; CFI, Bentler s comparative fit index; SRMR, standardized root mean square residual; RMSEA, root mean square error of approximation; CI, confidence interval around RMSEA. Depressive symptoms had the largest contribution to HRQoL, which was established through a direct relation with HRQoL and an indirect relation via the PS domain. Motor symptoms and autonomic dysfunction had indirect relations with HRQoL through both the PS and ADL domains. The indirect relation from psychiatric complications occurred through the PS domain, whereas the indirect relation of daytime sleepiness and motor complications occurred through the ADL domain. Figure 1. Model of health-related quality of life in PD Depressive symptoms Psychiatric complications 0.08 Motor symptoms 0.19 R 2 = Psychosocial wellbeing R 2 = 0.43 Autonomic dysfunction Motor complications R 2 = 0.57 Activities of Daily Living Health related Quality of Life 0.11 Daytime sleepiness Pain All path coefficients are statistically significant at P =0.05. Covariances between impairment domains are not shown for reason of readability. Chapter 3 41

9 Model evaluation with subdomains Axial symptoms was the only motor subdomain that contributed significantly to the PS domain and explained 31% of the variance. All three motor subdomains contributed significantly to the ADL domain explaining 60% of the variance, in which axial symptoms had the largest contribution and tremor the smallest contribution. Both motor fluctuations and dyskinesias contributed significantly to the ADL domain, explaining 14% of the variance. Only the subdomains GI, UR and PM of autonomic dysfunction contributed significantly to the ADL domain, and explained 32% of the variance. The subdomains GI, TR, UR, and CV contributed significantly to the PS domain, explaining 33% of the variance in the PS domain. The GI and UR subdomains were the most important contributors to both the PS and ADL domain. Model evaluation in subgroups To evaluate the influence of disease duration in the model, the path analysis was performed in two subgroups based on disease duration (disease duration shorter (N=205) or longer (N=173) than 10 years) (Table 5). Table 5. Significant domains in path analysis in PD subgroups. Disease duration < 10 year > 10 year N PS Depressive symptoms Autonomic dysfunction Motor symptoms Depressive symptoms Autonomic dysfunction Motor symptoms R 2 = 0.64 R 2 = 0.51 ADL Motor symptoms Autonomic dysfunction Motor complications Motor symptoms Autonomic dysfunction Motor complications R 2 = 0.44 R 2 = 0.58 HRQoL Psychosocial functioning Depressive symptoms Psychosocial functioning Depressive symptoms Pain ADL R 2 = 0.44 R 2 = 0.40 PS: Psychosocial functioning ADL: Activities of Daily Living HRQoL: Health related quality of life The models that emerged for each group were very similar, except for the pain and ADL, which lost their significant direct relation to HRQoL in patients with a disease duration longer than 10 years. Compared to the model that was based on all patients, the models for both disease duration groups lost the contribution of psychiatric complications to PS and DS to ADL. 42 A comprehensive model of Health-related Quality of Life in Parkinson s disease

10 Discussion HRQoL represents the overall experienced impact of the disease and its consequences on a person s wellbeing. Some diseases are expressed in one impairment domain, and their impact on disability and HRQoL is generally straightforward. PD, however, is characterized by a broad spectrum of primary disease-related motor and nonmotor manifestations. On top of these primary PD-related impairments, medical interventions may induce motor or psychiatric complications or DS. The impact of PD on patients HRQoL is thus determined by the complex interaction of the motor and nonmotor symptoms of the disease, the consequences of therapy and the functional consequences of the disease. This study shows that HRQoL in PD can be described by a good fitting model that disentangles the contributions of components in the pathway that links impairments and disabilities to HRQoL. Interestingly, on the disability level, the influence of the PS domain on HRQoL was larger than that of the ADL domain, underscoring the importance of psychosocial functioning in HRQoL of patients with PD. Most impairments exerted an indirect influence on HRQoL through one or both of the disability domains. Pain was the only impairment domain with only a direct influence on HRQoL and its impact on HRQoL has been reported earlier. 5 In line with other studies, our study highlights that depressive symptoms had the largest contribution to HRQoL, which was portrayed in our model by a direct influence on HrQoL as well as an indirect influence through the PS domain. As depressive symptoms have now been highlighted repeatedly as the main contributor to HRQoL in PD, it remains surprising that in both patient management and trials, depression is insufficiently prioritized. Both daytime sleepiness and motor complications were only related to ADL whereas autonomic dysfunction contributed to both physical (ADL) and psychosocial (PS) functioning. Within the autonomic domain, gastrointestinal and urinary symptoms had the largest influence on HRQoL. Within the motor complications, both motor fluctuations and dyskinesias were significant contributors to HRQoL. Motor symptoms had only indirect relations with HRQoL through both disability domains, and its total contribution to HRQoL was less than that of depression. Of motor symptoms, axial symptoms had the largest influence on both psychosocial and physical functioning. Motor symptoms and ADL, which till recently dominated the content of assessment scales in PD, were not the main contributors to HRQoL in patients with PD. However, it is important to keep in mind that dopaminergic replacement therapy aiming to alleviate part of the motor symptoms is the mainstay treatment of PD. It can be assumed that the contributions to the total model on HRQoL in PD would have been different, if this treatment had not been available. The impact of dopaminergic therapy on the model is further underscored by our finding that axial symptoms, which are mainly of nondopaminergic origin, had the largest influence on both the PS and ADL domain. Neither cognition nor night-time sleep, contributed to HRQoL. Consistent with former studies, 30,31 the univariate correlations between HRQoL and night-time sleep problems (r=-0.33), and between HRQoL and cognitive dysfunction (r=0.24) were significant, but their influence disappeared in the final model. It is likely that the strong association between night-time sleep problems and depressive symptoms (r= 0.49) resulted in the exclusion of night-time sleep problems from the model. 32 The finding that cognition does not play a role in HRQoL is surprising, but robust, as two other studies have revealed similar results in multiple linear regression analysis. 33,34 Contrary to night-time sleep problems, cognitive Chapter 3 43

11 dysfunction was in this study not strongly correlated to other domains of PD (all r < 0.40) and therefore the question remains how this finding can be explained. One explanation could be that cognitive decline played little role in this cohort, or that patients with cognitive impairment had missing values and where therefore not included in these analysis. Although the patients that were excluded had indeed more cognitive problems, they had also a worse HRQoL and the correlation between cognition and HRQoL was the same in both groups. Another explanation could be that the cognitive assessment describes the current cognitive status and this does not automatically reflect cognitive decline since the premorbid variance of normal cognition is large. For most other domains, like motor symptoms or psychiatric complications, a score of zero is expected before the onset of PD and a higher score therefore implies more severe problems. A longitudinal study that assesses the influence of change in cognition in relation to change in HRQoL is necessary to get more insight in this issue. The HRQoL model that was constructed appears robust, as it largely remained the same when evaluated in subgroups with a short and long disease duration. Depressive symptoms, psychosocial well-being, and autonomic dysfunction apparently remain important contributors to HRQoL along the course of the disease. The most prominent difference between the models of the two subgroups is the lack of a significant contribution of ADL to HRQoL in patients with long disease duration. A limitation of the study was the selective exclusion of patients with too many missing values. In addition, the stratification of the cohort based on age-at-onset and disease duration and the high recruitment of patients from academic hospitals (57%) may make the group less representative of the PD community. However, the remaining group of patients was still large and reflected the full spectrum of PD, with mean disease duration of 10 years and disease stages ranging from H&Y stage 1 to 5. The model incorporates only disease-specific aspects that explain 43 % of the variance in HRQoL. This implies that other aspects not incorporated in the model play a role as well. Educational level, mastery, or psychological adjustment have indeed been described to impact on HRQoL in PD. 33,35,36 Incorporating more variables in the model would on the one hand increase the amount of explained variance but conversely decrease the comprehensibility of the model. The main objective of this study was to evaluate the contribution of disease-specific factors to HRQoL, so as to highlight issues that require further attention in the management of patients with PD. Indeed, multiple factors affect HRQoL in patients with PD on symptomatic treatment. Research is warranted to develop and evaluate management strategies for the aspects that currently impact on HRQoL as psychosocial well-being, depressive symptoms, axial motor symptoms, gastrointestinal symptoms, and urinary symptoms. These findings call for a multidisciplinary approach in the care of these features. Acknowledgment This work was supported by a grant from Fonds Nuts/Ohra. This foundation played no part in the collection, analysis or interpretation of the data, or in writing the manuscript. 44 A comprehensive model of Health-related Quality of Life in Parkinson s disease

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